Here we use a variant of deterministic policy gradient algorithm DPG [1]} to directly capture the gradient of Q function respect to deterministic probability vector. DPG is specifically designed for continuous control. It handles the problem of instability of stochastic continuous policy gradient with the enhancement of efficiency. For high dimensional real world tasks, DDPG [2]} is developed. With the probability surrogate action, we are able to transform the stochastic discrete control tasks into deterministic continuous control tasks.
